EDBT 2026 Demo / reviewers in the wild / expert
Eva Piccininni
dblp:12/1175
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2000
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
2 papers |
Embedded and real-time systems · 60% Memory systems · 32% Performance modeling and evaluation · 8% | |
| Software engineering, system software, and programming languages
2 papers |
Compilers and program optimization · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Embedded and real-time systems › embedded processor
code compression |
0.0 | 1 | 2000 | Reducing Code Size with Run-Time Decompression · HPCA 2000 |
Memory systems › cache › CPU cache
instruction cache |
0.0 | 1 | 2000 | Reducing Code Size with Run-Time Decompression · HPCA 2000 |
Compilers and program optimization › code size reduction
code compression |
0.0 | 1 | 1999 | Evaluation of a High Performance Code Compression Method · MICRO 1999 |
Embedded and real-time systems
embedded processor |
0.0 | 1 | 1999 | Evaluation of a High Performance Code Compression Method · MICRO 1999 |
Compilers and program optimization
code size reduction |
0.0 | 1 | 2000 | Reducing Code Size with Run-Time Decompression · HPCA 2000 |
Performance modeling and evaluation
benchmarking |
0.0 | 1 | 1999 | Evaluation of a High Performance Code Compression Method · MICRO 1999 |
Methods — techniques the papers use, named apart from their topics
selective compression · 0.1cache miss profiling · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2000 | Reducing Code Size with Run-Time DecompressionabstractCompressed representations of programs can be used to improve the code density in embedded systems. Several hardware decompression architectures have been proposed recently. In this paper, we present a method of decompressing programs using software. It relies on using a software-managed instruction cache under control of the decompressor. This is achieved by employing a simple cache management instruction that allows explicit writing into a cache line. We also consider selective compression (determining which procedures in a program should be compressed) and show that selection based on cache miss profiles can substantially outperform the usual execution time based profiles for some benchmarks. Charles Lefurgy, Eva Piccininni, Trevor N. Mudge |
HPCA | 2 |
| 1999 | Evaluation of a High Performance Code Compression MethodabstractCompressing the instructions of an embedded program is important for cost-sensitive low-power control-oriented embedded computing. A number of compression schemes have been proposed to reduce program size. However, the increased instruction density has an accompanying performance cost because the instructions must be decompressed before execution. In this paper, we investigate the performance penalty of a hardware-managed code compression algorithm recently introduced in IBM's PowerPC 405. This scheme is the first to combine many previously proposed code compression techniques, making it an ideal candidate for study. We find that code compression with appropriate hardware optimizations does not have to incur much performance loss. Furthermore, our studies show this holds for architectures with a wide range of memory configurations and issue widths. Surprisingly, we find that a performance increase over native code is achievable in many situations. Charles Lefurgy, Eva Piccininni, Trevor N. Mudge |
MICRO | 2 |